Teaching

Introduction to Machine Learning (M2 MSV)

Introduction to Reinforcement Learning (M2 DS)

Foundations of Machine Learning (3A X)

Machine Learning 2 (1A DSB)

Lecture slides

Here are some slide sets that I have used for teaching. Do not hesitate to contact me if you have some remarks (or have spotted some errors)

Foundations of Machine Learning (3A, Ecole polytechnique)

Machine Learning 2 (M1 MScT Data Science for Business, X/HEC)

Introduction to Machine Learning (M2 Math pour les Sciences du Vivant, IPP/Paris Saclay)

Introduction to Reinforcement Learning (M2 Data Science, IPP)

  • Slides (2023-2024)

  • Slides (2022-2023)

  • Slides (2021-2022, previous version based on the book of Sutton and Barto)

Lecture Notes

Here are some documents that I have written for teaching purpose. Do not hesitate to contact me if you have some remarks (or have spotted some errors)

Foundations of Machine Learning (3A X)

Introduction to Reinforcement Learning (M2 DS)

Signal Processing (3A X)

Linking Approximation Theory and Statistical Estimation in Wavelet Image Processing (La Havane, Cuba)

Previous years

2022-2023
Introduction to Machine Learning (M2 MSV), Introduction to Reinforcement Learning (M2 DS), Foundation of Machine Learning (3A X), Machine Learning 2 (DSB)
2021-2022
Introduction to Machine Learning (M2 MSV), Introduction to Reinforcement Learning (M2 DS), Foundation of Machine Learning (3A X), Machine Learning 2 (DSB)
2020-2021
Introduction to Machine Learning (M2 MSV), Introduction to Reinforcement Learning (M2 DS), Introduction to Statistics (Bachelor X), Machine Learning 2 (3A X), Séminaire de modélisation (ENSAE)
2019-2020
Introduction to Machine Learning (M2 MSV), Introduction to Reinforcement Learning (M2 DS), Introduction to Statistics (Bachelor X), Machine Learning 2 (3A X), Nanterre
2018-2019
Introduction to Reinforcement Learning (M2 DS), Machine Learning 2 (3A X, M1 DSB), MicroBig, ATSI, ENSAE